A Hybrid Cascade Neuro–Fuzzy Network with Pools of Extended Neo–Fuzzy Neurons and its Deep Learning

Bodyanskiy, Yevgeniy; Tyshchenko, Oleksii K.

doi:10.2478/amcs-2019-0035

Cited by 17 publications

(5 citation statements)

References 25 publications

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“…In essence, data analysis occurs in each of these devices based on the attributes it collects. Their fusion is ensured using the principles of cascading (Bodyanskiy and Tyshchenko, 2019), where the predicted value of the searched attribute from the previous IoT device is passed to the next one as an additional feature. In addition, the authors used the Ito decomposition at each new level of the cascade.…”

Section: Designed Cascade-based Ensemblelearning Modelmentioning

confidence: 99%

A cascade ensemble-learning model for the deployment at the edge: case on missing IoT data recovery in environmental monitoring systems

Izonin,

Tkachenko,

Krak

et al. 2023

Front. Environ. Sci.

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In recent years, more and more applied industries have relied on data collection by IoT devices. Various IoT devices generate vast volumes of data that require efficient processing. Usually, the intellectual analysis of such data takes place in data centers in cloud environments. However, the problems of transferring large volumes of data and the long wait for a response from the data center for further corrective actions in the system led to the search for new processing methods. One possible option is Edge computing. Intelligent data analysis in the places of their collection eliminates the disadvantages mentioned above, revealing many advantages of using such an approach in practice. However, the Edge computing approach is challenging to implement when different IoT devices collect the independent attributes required for classification/regression. In order to overcome this limitation, the authors developed a new cascade ensemble-learning model for the deployment at the Edge. It is based on the principles of cascading machine learning methods, where each IoT device that collects data performs its analysis based on the attributes it contains. The results of its work are transmitted to the next IoT device, which analyzes the attributes it collects, taking into account the output of the previous device. All independent at-tributes are taken into account in this way. Because of this, the proposed approach provides: 1) The possibility of effective implementation of Edge computing for intelligent data analysis, that is, even before their transmission to the data center; 2) increasing, and in some cases maintaining, classification/regression accuracy at the same level that can be achieved in the data center; 3) significantly reducing the duration of training procedures due to the processing of a smaller number of attributes by each of the IoT devices. The simulation of the proposed approach was performed on a real-world set of IoT data. The missing data recovery task in the atmospheric air state data was solved. The authors selected the optimal parameters of the proposed approach. It was established that the developed model provides a slight increase in prediction accuracy while significantly reducing the duration of the training procedure. However, in this case, the main advantage is that all this happens within the bounds of Edge computing, which opens up several benefits of using the developed model in practice.

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Section: Designed Cascade-based Ensemblelearning Modelmentioning

confidence: 99%

A cascade ensemble-learning model for the deployment at the edge: case on missing IoT data recovery in environmental monitoring systems

Izonin,

Tkachenko,

Krak

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…To overcome this problem, deep learning algorithms have come into existence to extract features automatically. Different deep learning algorithms, i.e., deep neural networks (DNNs) (Faust et al, 2018;Mathews et al, 2018), deep belief networks (DBNs) (Mathews et al, 2018), and convolutional neural networks (CNNs) (Acharya et al, 2017c;Bodyanskiy and Tyshchenko, 2019) are utilised in the earlier methods. ECG beats are classified into N, S, and V classes using ordinal pattern entropies by Bidias á Mougoufan et al (2021).…”

Section: Review Of the Literature And Motivationsmentioning

confidence: 99%

A hybrid approach of a deep learning technique for real-time ECG beat detection

Patro,

Prakash,

Samantray

et al. 2022

International Journal of Applied Mathematics and Computer Scien

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This paper presents a new customized hybrid approach for early detection of cardiac abnormalities using an electrocardiogram (ECG). The ECG is a bio-electrical signal that helps monitor the heart's electrical activity. It can provide health information about the normal and abnormal physiology of the heart. Early diagnosis of cardiac abnormalities is critical for cardiac patients to avoid stroke or sudden cardiac death. The main aim of this paper is to detect crucial beats that can damage the functioning of the heart. Initially, a modified Pan-Tompkins algorithm identifies the characteristic points, followed by heartbeat segmentation. Subsequently, a different hybrid deep convolutional neural network (CNN) is proposed to experiment on standard and real-time long-term ECG databases. This work successfully classifies several cardiac beat abnormalities such as supra-ventricular ectopic beats (SVE), ventricular beats (VE), intra-ventricular conduction disturbances beats (IVCD), and normal beats (N). The obtained classification results show a better accuracy of 99.28% with an F1 score of 99.24% with the MIT-BIH database and a descent accuracy of 99.12% with the real-time acquired database.

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“…In subsequent layers, these neurons are connected to the previous layer in a "one-to-all" relation. This is a typical connection structure for shallow (Chang, 2015;Bodyanskiy and Tyshchenko, 2019) or fully connected neural networks (Basha et al, 2020). In this type of neural network, the hidden layer neurons are of the FFF type, while in the output layer, classical neurons are found exclusively and have a linear activation function.…”

Section: Fuzzy Flip-flop Neural Networkmentioning

confidence: 99%

A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks

Kowalski

Sloczynski

2021

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The aim of the presented study is to investigate the application of an optimization algorithm based on swarm intelligence to the configuration of a fuzzy flip-flop neural network. Research on solving this problem consists of the following stages. The first one is to analyze the impact of the basic internal parameters of the neural network and the particle swarm optimization (PSO) algorithm. Subsequently, some modifications to the PSO algorithm are investigated. Approximations of trigonometric functions are then adopted as the main task to be performed by the neural network. As a result of the numerical verification of the problem, a set of rules are developed that can be helpful in constructing a fuzzy flip-flop type neural network. The obtained results of the computations significantly simplify the structure of the neural network in relation to similar conditions known from the literature.

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A Hybrid Cascade Neuro–Fuzzy Network with Pools of Extended Neo–Fuzzy Neurons and its Deep Learning

Cited by 17 publications

References 25 publications

A cascade ensemble-learning model for the deployment at the edge: case on missing IoT data recovery in environmental monitoring systems

A cascade ensemble-learning model for the deployment at the edge: case on missing IoT data recovery in environmental monitoring systems

A hybrid approach of a deep learning technique for real-time ECG beat detection

A modified particle swarm optimization procedure for triggering fuzzy flip-flop neural networks

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